{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "        5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
       "        5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
       "        6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
       "        6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
       "        7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
       "        7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
       "        8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
       "        8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
       "        9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
       "        9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 100)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "siny = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosy = np.cos(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, siny, label=\"sin(x)\")\n",
    "plt.plot(x, cosy, color=\"red\", linestyle=\"--\", label=\"cos(x)\")\n",
    "# 坐标信息\n",
    "plt.xlabel(\"x axis\")\n",
    "plt.ylabel(\"y axis\")\n",
    "# 显示图例\n",
    "plt.legend()\n",
    "# 设置标题\n",
    "plt.title(\"trigonometric function\")\n",
    "# 设置坐标范围\n",
    "plt.xlim(-1, 11)\n",
    "plt.ylim(-2, 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
